A macro-micro manipulator system, which is composed of a long flexible-link manipulator as a macro manipulator and a relatively smaller rigid manipulator attached to the tip of the macro one as a micro manipulator, is employed to improve the motion precision of a flexible space manipulator. At the same time the macro-micro manipulator system preserves the capabilities of large work volume, high operation speed and low energy consumption of the flexible space manipulator. In this study, using the forward/inverse kinematics and a perturbation principle, an error compensation approach is proposed to eliminate as far as possible the endpoint errors of a flexible system through the fast and fine adjustment of the motion of the micro manipulator. A feedback model-based control scheme is proposed to realize the endpoint trajectory tracking of the flexible macro-micro manipulator system. Experimental results on a planar flexible 2-DOF macro/2-DOF micro manipulator system are presented herein to show the effectiveness of the proposed methods.

Based on advantage learning and fuzzy inference systems (FISs), fuzzy advantage learning (FAL) is presented. In FAL, a FIS is applied to introduce strong generalization ability and to generate continuous actions for advantage learning. This approach is illustrated and compared to other reinforcement learning methods. The simulation study on inverted pendulum control shows its effectiveness with faster learning speed.

This paper considers adaptive fuzzy control of robotic manipulators based on sliding mode. It is first shown that an adaptive fuzzy system with the system representative point (RP, or as is often termed, a switching function in variable structure control (VSC) theory) and its derivative as inputs, can approximate the robot nonlinear dynamics in the neighborhood of the switching hyperplane. Then a new method for designing an adaptive fuzzy control system based on sliding mode is proposed for the trajectory tracking control of a robot with unknown nonlinear dynamics. The system stability and tracking error convergence are also proved by Lyapunov techniques.